1. Detailed explanation of thermal calculation model

The heat dissipation calculation of closed cooling towers needs to consider both sensible heat and latent heat exchange. The sensible heat part follows Fourier's law: Q=U×A×ΔTlm, where the total heat transfer coefficient U value depends on the pipe material and the dirt coefficient, and the U value of clean copper pipe is about 380W/(m²·K). The latent heat part adopts the McKell equation: Q=m×hfg×(W2-W1), hfg is 2260kJ/kg, and W is the air humidity difference.

Take an injection molding machine with a heat dissipation demand of 500kW as an example: when the wet bulb temperature is 28℃, a copper tube coil with a heat exchange area of ​​120m² (6-row design) is required, the spray water volume is 3.8kg/s, and the headwind speed is 2.3m/s. If the wet bulb temperature rises to 32℃, the heat exchange area needs to be increased by 15% under the same load.

2. Antifreeze protection system design

An ethylene glycol solution antifreeze system must be configured in cold areas. The freezing point of 30% ethylene glycol solution can drop to -15°C, but it will increase the pump head by 20% and reduce the heat transfer efficiency by 8%. It is recommended to adopt a two-stage heating scheme: start electric heating (20W/m power density) at -5°C, and activate the auxiliary steam heating coil at -15°C.

The operation and maintenance records of a chemical plant in a cold area show that after using 40% ethylene glycol solution + intelligent temperature control system, it can still operate stably at extreme temperatures of -30°C. Key measures include: the pipeline uses a 50mm thick polyurethane insulation layer, all low points are equipped with automatic drain valves, and temperature sensors are arranged at 3m intervals and redundantly configured.

III. Intelligent control system architecture

Modern closed cooling towers should be equipped with a four-level control system:

Basic layer: PLC real-time monitoring of 16 temperature/pressure measurement points

Regulation layer: PID algorithm controls fan speed (regulation accuracy 1%)

Optimization layer: Dynamic pre-adjustment of operating parameters based on weather forecast